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1978 年英国寄宿学校流感爆发:经典 SEIR 模型的失败之处。

The 1978 English boarding school influenza outbreak: where the classic SEIR model fails.

机构信息

Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR, People's Republic of China.

Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, People's Republic of China.

出版信息

J R Soc Interface. 2024 Nov;21(220):20240394. doi: 10.1098/rsif.2024.0394. Epub 2024 Nov 20.

DOI:10.1098/rsif.2024.0394
PMID:39563495
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11576841/
Abstract

Previous work has failed to fit classic SEIR epidemic models satisfactorily to the prevalence data of the famous English boarding school 1978 influenza A/H1N1 outbreak during the children's pandemic. It is still an open question whether a biologically plausible model can fit the prevalence time series and the attack rate correctly. To construct the final model, we first used an intentionally very flexible and overfitted discrete-time epidemiologic model to learn the epidemiological features from the data. The final model was a susceptible () - exposed () - infectious () - confined-to-bed () - convalescent () - recovered () model with time delay (constant residence time) in and compartments and multi-stage (Erlang-distributed residence time) in and compartments. We simultaneously fitted the reported and prevalence curves as well as the attack rate (proportion of children infected during the outbreak). The non-exponential residence times were crucial for good fits. The estimates of the generation time and the basic reproductive number ([Formula: see text]) were biologically reasonable. A simplified discrete-time model was built and fitted using the Bayesian procedure. Our work not only provided an answer to the open question, but also demonstrated an approach to constructive model generation.

摘要

先前的工作未能令人满意地将经典 SEIR 传染病模型拟合到著名的 1978 年英国寄宿学校甲型流感 A/H1N1 爆发期间的流行数据中。一个生物学上合理的模型是否能正确拟合流行时间序列和发病率仍然是一个悬而未决的问题。为了构建最终模型,我们首先使用一个故意非常灵活和过拟合的离散时间流行病学模型从数据中学习流行病学特征。最终模型是一个具有时间延迟(固定居留时间)的易感者()-暴露者()-感染者()-卧床隔离者()-恢复期()-康复者()模型,在 和 隔间中有多阶段(爱尔朗分布居留时间),在 和 隔间中有多阶段(爱尔朗分布居留时间)。我们同时拟合了报告的 和 流行曲线以及发病率(爆发期间感染儿童的比例)。非指数居留时间对于良好的拟合至关重要。生成时间和基本繁殖数([公式:见正文])的估计值在生物学上是合理的。使用贝叶斯过程构建并拟合了一个简化的离散时间模型。我们的工作不仅回答了悬而未决的问题,还展示了一种建设性的模型生成方法。

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The origins of COVID-19 pandemic: A brief overview.新冠疫情的起源:简要概述。
Transbound Emerg Dis. 2022 Nov;69(6):3181-3197. doi: 10.1111/tbed.14732. Epub 2022 Oct 20.
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The Johns Hopkins University Center for Systems Science and Engineering COVID-19 Dashboard: data collection process, challenges faced, and lessons learned.约翰斯·霍普金斯大学系统科学与工程中心 COVID-19 数据仪表盘:数据收集流程、面临的挑战和经验教训。
Lancet Infect Dis. 2022 Dec;22(12):e370-e376. doi: 10.1016/S1473-3099(22)00434-0. Epub 2022 Aug 31.
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Cross-reactive immunity potentially drives global oscillation and opposed alternation patterns of seasonal influenza A viruses.交叉反应性免疫可能驱动季节性甲型流感病毒的全球震荡和相反交替模式。
Sci Rep. 2022 May 25;12(1):8883. doi: 10.1038/s41598-022-08233-w.
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Brazil's COVID-19 Epicenter in Manaus: How Much of the Population Has Already Been Exposed and Are Vulnerable to SARS-CoV-2?巴西亚马孙州首府马瑙斯市或成新冠疫情新“震中”:当地多少人已暴露于新冠病毒且易感染?
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What the Diamond Princess taught the world about covid-19.“钻石公主”号邮轮事件给世界带来的关于新冠疫情的教训。
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